Automated code inspection for performance
OPEN CATALOG
Best practices for performance
Docs Category: C
- PWD002: Unprotected multithreading reduction operation
- PWD003: Missing array range in data copy to the GPU
- PWD004: Out-of-memory-bounds array access
- PWD005: Array range copied to or from the GPU does not cover the used range
- PWD006: Missing deep copy of non-contiguous data to the GPU
- PWD007: Unprotected multithreading recurrence
- PWD008: Unprotected multithreading recurrence due to out-of-dimension-bounds array access
- PWD009: Incorrect privatization in OpenMP parallel region
- PWD010: Incorrect sharing in OpenMP parallel region
- PWD011: Missing OpenMP lastprivate clause
- PWR001: Declare global variables as function parameters
- PWR002: Declare scalar variables in the smallest possible scope
- PWR003: Explicitly declare pure functions
- PWR004: Declare OpenMP scoping for all variables
- PWR005: Disable default OpenMP scoping
- PWR006: Avoid privatization of read-only variables
- PWR009: Use OpenMP teams to offload work to GPU
- PWR010: Avoid column-major array access in C/C++
- PWR012: Pass only required fields from derived data types as parameters
- PWR013: Avoid copying unused variables to or from the GPU
- PWR014: Out-of-dimension-bounds matrix access
- PWR015: Avoid copying unnecessary array elements to or from the GPU
- PWR016: Use separate arrays instead of an Array-of-Structs
- PWR017: Using countable while loops instead of for loops may inhibit vectorization
- PWR018: Call to recursive function within a loop inhibits vectorization
- PWR019: Consider interchanging loops to favor vectorization by maximizing inner loop’s trip count
- PWR020: Consider loop fission to enable vectorization
- PWR021: Consider loop fission with scalar to vector promotion to enable vectorization
- PWR022: Move invariant conditional out of the loop to facilitate vectorization
- PWR023: Add ‘restrict’ for pointer function parameters to hint the compiler that vectorization is safe
- PWR024: Loop can be rewritten in OpenMP canonical form
- PWR025: Consider annotating pure function with OpenMP ‘declare simd’
- PWR026: Annotate function for OpenMP offload
- PWR027: Annotate function for OpenACC offload
- PWR028: Remove pointer increment preventing performance optimization
- PWR029: Remove integer increment preventing performance optimization
- PWR030: Remove pointer assignment preventing performance optimization for perfectly nested loops
- PWR031: Replace call to pow by multiplication, division and/or square root
- PWR032: Avoid calls to mathematical functions with higher precision than required
- PWR033: Move invariant conditional out of the loop to avoid redundant computations
- PWR034: avoid strided array access to improve performance
- PWR035: avoid non-consecutive array access to improve performance
- PWR036: Avoid indirect array access to improve performance
- PWR037: Potential precision loss in call to mathematical function
- PWR039: Consider loop interchange to improve the locality of reference and enable vectorization
- PWR040: Consider loop tiling to improve the locality of reference
- PWR042: Consider loop interchange by promoting the scalar reduction variable to an array
- PWR043: Consider loop interchange by replacing the scalar reduction
- PWR044: Avoid unnecessary floating-point data conversions involving constants
- PWR045: Replace division with a multiplication with a reciprocal
- PWR046: Replace two divisions with a division and a multiplication
- PWR048: Replace multiplication/addition combo with an explicit call to fused multiply-add
- PWR050: Consider applying multithreading parallelism to forall loop
- PWR051: Consider applying multithreading parallelism to scalar reduction loop
- PWR052: Consider applying multithreading parallelism to sparse reduction loop
- PWR053: Consider applying vectorization to forall loop
- PWR054: Consider applying vectorization to scalar reduction loop
- PWR055: Consider applying offloading parallelism to forall loop
- PWR056: Consider applying offloading parallelism to scalar reduction loop
- PWR057: Consider applying offloading parallelism to sparse reduction loop
- PWR060: Consider loop fission to separate gather memory access pattern
- RMK001: loop nesting that might benefit from hybrid parallelization using multithreading and SIMD
- RMK002: Loop nesting that might benefit from hybrid parallelization using offloading and SIMD
- RMK003: potential temporary variable for the loop which might be privatizable, thus enabling the loop parallelization
- RMK007: SIMD opportunity a within multi-threaded region
- RMK008: SIMD opportunity within an offloaded region
- RMK009: Outline loop to increase compiler and tooling code coverage
- RMK010: The vectorization cost model states the loop is not a SIMD opportunity due to strided memory accesses in the loop body
- RMK012: The vectorization cost model states the loop is not a SIMD opportunity because conditional execution renders vectorization inefficient
- RMK013: The vectorization cost model states the loop is not a SIMD opportunity because loops with low trip count unknown at compile time do not benefit from vectorization
- RMK014: The vectorization cost model states the loop is not a SIMD opportunity due to unpredictable memory accesses in the loop body
- RMK015: Tune compiler optimization flags to increase the speed of the code
- RMK016: Tune compiler optimization flags to avoid potential changes in floating-point precision
